NDLI: Data mining of power transformer database using self-organising maps

Content Provider	IEEE Xplore Digital Library
Author	Obu-Cann, K. Fujimura, K. Tokutaka, H. Ohkita, M. Inui, M. Ikeda, Y.
Copyright Year	2001
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Tottori Univ., Japan (Obu-Cann, K.)
Abstract	Data mining is part of a large area of recent research in artificial intelligence and information processing and management otherwise known as knowledge discovery in databases (KDD). The main aim here is to identify new information or knowledge from a database in which the dimensionality or amount of data is so large that it is beyond human comprehension. The self-organising map (SOM) is used to analyse a power transformer database from one of the electric energy providers in Japan. Furthermore, the regression aspect of SOM is also tested. Regression is achieved by searching for the Best Matching Unit (BMU) using the known vector components. Some attempts have also been made in using SOM to predict transformer oil temperature changes. Conventionally, oil temperature changes in a power distribution transformer, are predicted using explicit numerical calculations. This paper applies the self-organising maps to the prediction of oil temperature changes.
Sponsorship	China Assoc. Sci. & Technol.(CAST)
Starting Page	44
Ending Page	49
File Size	509484
Page Count	6
File Format	PDF
ISBN	0780370104
DOI	10.1109/ICII.2001.983717
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-10-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature Databases Humans Information processing Knowledge management Information management Data mining Power transformers Artificial intelligence Petroleum
Content Type	Text
Resource Type	Article

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